Abstract: A method for packing virtual machines onto host devices may calculate scarcity values for several different parameters. A host's scarcity for a parameter may be determined by multiplying the host's capacity for a parameter with the overall scarcity of that parameter. The sum of a host's scarcity for all the parameters determines the host's overall scarcity. Hosts having the highest scarcity are attempted to be populated with a group of virtual machines selected for compatibility with the host. In many cases, several different scenarios may be evaluated and an optimal scenario implemented. The method gives a high priority to those virtual machines that consume scarce resources, with the scarcity being a function of the available hardware and the virtual machines that may be placed on them.

Abstract: A method for packing virtual machines onto host devices may calculate scarcity values for several different parameters. A host's scarcity for a parameter may be determined by multiplying the host's capacity for a parameter with the overall scarcity of that parameter. The sum of a host's scarcity for all the parameters determines the host's overall scarcity. Hosts having the highest scarcity are attempted to be populated with a group of virtual machines selected for compatibility with the host. In many cases, several different scenarios may be evaluated and an optimal scenario implemented. The method gives a high priority to those virtual machines that consume scarce resources, with the scarcity being a function of the available hardware and the virtual machines that may be placed on them.

Abstract: A method for packing virtual machines onto host devices may calculate scarcity values for several different parameters. A host's scarcity for a parameter may be determined by multiplying the host's capacity for a parameter with the overall scarcity of that parameter. The sum of a host's scarcity for all the parameters determines the host's overall scarcity. Hosts having the highest scarcity are attempted to be populated with a group of virtual machines selected for compatibility with the host. In many cases, several different scenarios may be evaluated and an optimal scenario implemented. The method gives a high priority to those virtual machines that consume scarce resources, with the scarcity being a function of the available hardware and the virtual machines that may be placed on them.

Abstract: A method for packing virtual machines onto host devices may calculate scarcity values for several different parameters. A host's scarcity for a parameter may be determined by multiplying the host's capacity for a parameter with the overall scarcity of that parameter. The sum of a host's scarcity for all the parameters determines the host's overall scarcity. Hosts having the highest scarcity are attempted to be populated with a group of virtual machines selected for compatibility with the host. In many cases, several different scenarios may be evaluated and an optimal scenario implemented. The method gives a high priority to those virtual machines that consume scarce resources, with the scarcity being a function of the available hardware and the virtual machines that may be placed on them.

Abstract: A method for packing virtual machines onto host devices may calculate scarcity values for several different parameters. A host's scarcity for a parameter may be determined by multiplying the host's capacity for a parameter with the overall scarcity of that parameter. The sum of a host's scarcity for all the parameters determines the host's overall scarcity. Hosts having the highest scarcity are attempted to be populated with a group of virtual machines selected for compatibility with the host. In many cases, several different scenarios may be evaluated and an optimal scenario implemented. The method gives a high priority to those virtual machines that consume scarce resources, with the scarcity being a function of the available hardware and the virtual machines that may be placed on them.

Abstract: A virtual machine management system may perform a three phase migration analysis to move virtual machines off of less efficient hosts to more efficient hosts. In many cases, the migration may allow inefficient host devices to be powered down and may reduce overall energy costs to a datacenter or other user. The migration analysis may involve performing a first consolidation, a load balancing, and a second consolidation when consolidating virtual machines and freeing host devices. The migration analysis may also involve performing a first load balancing, a consolidation, and a second load balancing when expanding capacity.

Abstract: A bitmap may be transformed from one color scheme to another. In one example, the bitmap is used as part of a user interface (UI), and the colorization is performed to reflect a user's (or some other entity's) choice of color scheme. To perform the colorization, a bitmap may be converted from the red-green-blue (RGB) color space to the hue-saturation-lightness (HSL) color space. The hue may then be rotated toward the selected color scheme. Lightness and alpha (opacity) may be gamma-adjusted toward the selected color scheme. Saturation may be linearly adjusted upward or downward in the direction of the selected color scheme. The transformed HSL representation of the image may then be converted from the HSL space back to the RGB space. The new RGB bitmap may then be used to render one or more UI elements.

Abstract: A virtual machine management system may perform a three phase migration analysis to move virtual machines off of less efficient hosts to more efficient hosts. In many cases, the migration may allow inefficient host devices to be powered down and may reduce overall energy costs to a datacenter or other user. The migration analysis may involve performing a first consolidation, a load balancing, and a second consolidation when consolidating virtual machines and freeing host devices. The migration analysis may also involve performing a first load balancing, a consolidation, and a second load balancing when expanding capacity.

Abstract: A method for packing virtual machines onto host devices may calculate scarcity values for several different parameters. A host's scarcity for a parameter may be determined by multiplying the host's capacity for a parameter with the overall scarcity of that parameter. The sum of a host's scarcity for all the parameters determines the host's overall scarcity. Hosts having the highest scarcity are attempted to be populated with a group of virtual machines selected for compatibility with the host. In many cases, several different scenarios may be evaluated and an optimal scenario implemented. The method gives a high priority to those virtual machines that consume scarce resources, with the scarcity being a function of the available hardware and the virtual machines that may be placed on them.